{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_i = 1\n",
    "fn_label = './paper_data/test0{0}.label.png'.format(test_i)\n",
    "fn_test = './paper_data/test0{0}.png.test.png'.format(test_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_test = Image.open(fn_test).convert('L')\n",
    "img_label = Image.open(fn_label).convert('L').resize(img_test.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=1252x834 at 0x7F6DEDE236D8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABOQAAANCCAAAAADjSEy9AAAWk0lEQVR4nO3dz6vl913H8fc3k46dpA0TLG1sDZ0ZaBatUCoIpSC5sxG00CrYPyCzEopo/oFLG3DfhdkoxLpy4UIE7fZOoJsubFJBSqk6gxbtxjb2R9JOZ+a6mMbeM5lkzrm93+/7fF+fx2Mjp965533Juc/z+XzPPZ8zHRdArke6BwCYk8gB0UQOiPboxq2XbvZMwS/phe4BYG9ZyQHRRA6IJnJAtM3I+aM5IMxm5O42TQEwk83ITU1TAMzkkXe4BbB6sgZEEzkgmsgB0UQOiCZyQDSRA6KJHBBN5IBoIgdEEzkgmsgB0UQOiCZyQDSRA6KJHBBtcuI5kMxKDogmckA0kQOiiRwQTeSAaI9u3vSZhECQYys5IJwPlwaiPXr//3Dvj4MPz/AuXjjD73UWrtwofwH9pqmODrpnYBWmWt/vzVTPlO0qEO4tK7lRfPeNs/tel8/uWzVJXrkzumEj95WbZ/e9/FrD/rJdBaINu5IDZnT8/VtVVecvPlJVt1785kf/+Nz////uuzk3kQPO3P1XeZ/63hff4eZOdr46ZLsKRFsgctfvzn8fAA+2QOT+2fsogDYLRO53RA5os0Dkrsx/FwBvY4HInZ//LgDehldXgWgiB0QTOSCayAHRRA6IJnJANJEDookcEM1RS8CZ26fTsq3kgGhvs5Lbpw4DnJ6VHBBt2Gtyn1j/5wieISt3co0bue4BgEXYrgLR3rKS+0bHFIu6NcIPub1/e7J7AtZihb83t6uqjk/qngjgDL3v+PjYdhWIdbdckwOCva8ecE0uf8t65cYAP+S2pjo66J6BVZhWGId7HxRoJQdEEzkgmsgB0YZ9xwMnHXYPsBTvXxuQyAFdNp5d53oGsl0FookcEE3kgGgiB0QTOSCayAHRRA6IJnJANJEDookcEE3kgGgiB0QTOSCayAHRHLVEOWaNZCIHdFnk2dV2FYgmckA0kQOiuSY3uju3uydgLVb4UDmuqmnjQ7HX+CHZO3vm2wP8kNuaugeAWR0PuV1d4dMRcGoDRg4YiWtyozs66J6AVVjjtaypnikrOSCcyAHRRA6IJnJANC88UFWH3QPszLkpCTYed7P9J7WSA6KJHBBN5IBoIgdEEzkgmsgB0UQOiCZyQDSRA6KJHBBN5IBoIgdEEzkgmsgB0Ry1BDRZ5sAsKzkgmpUc5QhKklnJAdFEDogmckA0kQOiiRwQbTreuFV1/HZfGePKjQF+yG1N3QPArI6HXMn5qxkYyYC/8be7B9gvf/Yb3ROwCp+t+vvuGXb12fpQDRk5NnzqoHsC1uIz3QPs7vEacrsKjGTYldxLNzvu1dunYGlWckA0kQOiDbtd5aTD7gHeZD8/lpdO3rg2052IHNDl5hJ3YrsKRBM5IJrIAdFEDogmckA0kQOiiRwQTeSAaCIHRBM5IJrIAdFEDogmckA0kQOiOWoJ6LLI+YEiRz30sXbrL+/80fk3bxx/7e+e/twHtvrqjX8HPUSOhzv/+RM3pk9+ctuv3vh30GPYyP1a9wDAIoaN3O92DwAswqurQDSRA6INu13l5652D8BqTN0DnI6VHBBtwMid6x4AWMjrNeR29U73APvly7/VPQGr8LGqf+meYVcfq/M1ZOTY8OGPdk/AWqzwofJoDbldBUYickA0kQOiiRwQzQsPVNXhaf/hIueBEWvjgTfXg8lKDogmckA0kQOiiRwQTeSAaCIHRBM5IJrIAdFEDogmckA0kQOiiRwQTeSAaCIHRBM5IJrz5IAui5xHaCUHRLOSoxzwSzIrOSCayAHRbFdHd7V7AFZj6h5gdz+rIVdy57oHABZyu4Zcyd3pHmC/fPxi9wSswstVz3bPsKuX60INGTk2fOmgewJWYaq63j3Dru7trwfcrgIjETkgmsgB0UQOiOaFB6qqDrsHeJCr/oYv3cbjbq43F1rJAdFEDogmckA0kQOiiRwQTeTYW3e7ByCCyAHRRI69ddw9ABFEDogmckA0kQOiiRwQTeSAaCLH3vKRQ5wFkQOiOU8O6DLXEXIbRI6qque6B3iQJ7sHIILIUVV1uXsAmItrckA0kQOiTRtvgp5GeFP009/pngBYxhP/O+RKbuoeAFjIcQ0ZOa+1wCjO16i/8fl78m1NdXTQPQOrsMZrWVP9ag25kgNGInJANJEDoo15TY77HXYPcGYWeTckayJyQJeNJ9e5np9sV4FoIgdEEzkgmsgB0UQOiCZyQDSRA6KJHBBN5IBoIgdEEzkgmsgB0UQOiCZyQDRHLQFdFjn9T+SoctQkwWxXgWgiB0SzXR3dl693T8BafKF7gNOZNj4vdo2fH7uzKzcG+CG3NXUPAHN65ltDblctXmEUP6ghf+Nvdw+wX44OuidgFda4zZvqiRpyJQeMROSAaCIHRBM5IJrIAdEGfHWVB7jRPcASLncPwP0OT96Y6w3UIkdV1V91D7AEpxCMyXYViCZyQDSRA6KJHBBN5IBoIgdEEzkgmsgB0UQOiCZyQDSRA6KJHBBN5IBoTiEBulxa4k5EDuhybYk7sV0FolnJUeU8SYJZyQHRRA6IJnJANJEDonnhgevdA7AS17sHOB2R42r3AKzESh8pA0ZuwB/5nfzB+e4JYDY/qiF/4293D7BfXvpU9wSswlNV3+2eYVdP1WM1ZOTY8OQHuidgLVb4UHmkvLoKhBM5IJrIAdFEDojmhQeqqg63/cJLi5wAxiBeOnljroeWyAFdbi5xJ7arQDSRA6KJHBBN5IBoIgdEEzkgmsgB0UQOiCZyQDSRA6KJHBBN5IBoIgdEEzkgmqOWgC4vLHEnVnJANCs5qhZ6RoUOVnJANJEDotmuju5rr3dPwFp8pXuA3d2qqun45P8yVR2/zRfn+Mi/DvBDbmvqHgDm9PR/DLldvdM9ALCQC2W7yscvdk/AKrxc9Wz3DLt6uapEji8ddE/AKkxV17tn2NW9qzEDbleBkYgcEE3kgGiuyVFVddg9wO68Ey3AxuNurv+iVnJANJEDookcEE3kgGgiB0QTOSCayAHRRA6IJnJANJEDookcEE3kgGgiB0QTOSCao5aALoucl2UlB0QTOSCa7SpVjtklmJUcEE3kgGgiB0RzTW50f3qxewLW4qB7gNMRudF9o3sAVuPl7gFOx3YViPWzGnIld657gP3ykfd2T8AqfL3qN7tn2NXXR43cne4B9stfHHRPwCpMVf/UPcOupnqsbFeBcCIHRBM5IJrIAdEGfOGBBzjsHmArjhHgFEQO6LLx5DrXc5jtKhBN5IBoIgdEEzkgmsgB0UQOiCZyQDSRA6KJHBBt5Hc87PNbmbyBCc6IlRwQTeSAaCIHRBM5IJrIAdFGfnWVX7jaPQBDWuSvCESOKpEj2MDb1ePuAYAFDBy5n3YPACxg4Mj9sHsAYAEDXpN7s+uvdQ6xJ667Gsf2pu4BdvdGDbmSu/vz/zvgjw4DGvg3/cnuAYCZXaght6tvek/3AHvgoOrooHsIVmFa4R8k3NtfD7ySe1f3AMACBo7cCq+iAjsbOHLACAa+JscJ+3xK8i/hqj+Q2W8bj7u53shqJQdEEzkgmsgB0UQOiDbyCw8+9g8GYCVHsLsP/xLiiRzBnBmIyBHtVvcA7AGRI9i57gHYAyJHMCfNIHJEW93ZQMxA5Agmcogc0Ty88SgAwo38jgeg16Ul7kTkgC7XlrgTkaOq6rlT/Jvb/3D3M3v++PGJbIgc91w+zT96/qyngBl44QGIJnJANJEDookcEE3kgGheXR3a9SqfTMq2pu4BTsdKDoj147KSG9xB1XOn+hM5hnO4wo9+OqyqqmnjMJpphMNprtwY4Ifc1lRHB90zsAprjMNUz3zLdhUIJ3JANJEDookcEE3kgGj+hATocnjyxlx/oSJyVN33YFuH1f3RFk1sV4FoIgdEEzkgmsgB0UQOiCZyQDSRA6KJHBBN5IBoIgdEEzkgmsgB0UQOiCZyQDRHLQFdFjkvy0oOiCZyQDTbVaocs0swKzkgmsgB0R68XV3+Y01sl7q82j0Aq3G9e4DdvV6uyfF89wCsxtXuAXb3nbJdBcINuJKbugfYL7//oe4JWIUXqz7fPcOuXqyna8jIHXcPsF/+5KB7Albhxao/755hVy/WhbJdBcKJHBBN5IBoIgdEG/CFBx5giz//9vfarJPIAV02nlznehq1XQWiiRwQTeSAaCLHln7WPQCcisixpde7B4BTETm29O7uAeBURI4tne8eAE5F5NiSI6pYJ5EDookcEE3kgGgiB0R78Bv0HTgBhLCSA6KJHBDNeXJUVT3XPQBDWuTCmMhRVXW5ewCYi+0qEE3kgGi2q0O7XnW1ewZWY6VvXx5wJafrv/Cj7gFgVj+oISN3u3uAPfKe7gFgVu8ty5rBHVQdHXQPwSpMVcfdM+xqqqmGXMkBIxE5IJrIAdFckwO6HJ68Mdd7vESOqvsebFu7dO2Mx4CzZ7sKRBM5qupu9wAwG5GjVvt+HdiCyFGnjtzq/jiUEYkcEE3kOD27XFZA5IBoIgdEEzkgmsgB0UQOiCZyQDSRA6KJHBDNUUtAl7mOkNtgJQdEs5KjaqFnVOhgJQdEEzkgmu3q6K52D8BqrPTUGSs5INZPasjIDfgjw6COa8jtqg9t2XB00D0BqzCt8Lj7qS6UZQ0QTuSAaCIHRBM5INqALzzwAIe7fLH3gHFGjk7emOtPNkUO6LJI5GxXgWgiB0QTOSCayAHRRA6IJnJANJEDookcEE3kgGgiB0QTOSCayAHRRA6IJnJANEctAV0WOZrQSg6IZiVHlcN+CWYlB0QTOSCayAHRBrwmd6tq6p5hj8z16SHkWeHvzRs15EruXPcAwELOlcgBwc7XkNvVqjruHmBvTHV00D0DqzCt8Pfm3v56wJUcMBKRA6KJHBBtzGty3O9whu/prWI8zMbjbq4HjJUcEE3kgGgiB0QTOSCayAHRRA6IJnJANJEDookcEE3kgGgiB0QTOSCayAHRRA6I5qgloMulJe5E5IAu15a4E5Gjquq57b7s+Jtfff+zT847CpwtkaOq6vKWX3fl07OOAWfPCw9ANJEDookcEE3kgGheeBjazaovX+8egrX4QvcApyNyQ7tZ9dfdM7AaX+we4HQG3K4O+CPDoN6oIVdyd7sH2CMHVV/4WPcQrMLnqv62e4Zdfa4eqSEjx4ZnD7onYC3+sHuA3f1K2bsB4UQOiCZyQDSRA6J54YGqqjqc85tfvTrnd2e9Nh52L8x0J1ZyQDSRA6KJHBBN5IBoIgdEEzkgmsgB0UQOiCZyQDSRA6KJHBBN5KiqOu4eAOYiclSJHMGGPYXkpZvdE7y9uU5jeAee7Ig1bOSAds8tcSciB3S5vMSdiBxVtftT6vE3v/r+Z5/c8ou3/TqYgchRVad4Sr3y6TnGgDPngjMQTeSAaCIHRBM5IJrIAdG8ujo6H4nKtqbuAU5nwJXcSv9LATt7o4aMnPeiw0hsV0d3dNA9AaswrXB9MNWFGnIlB4xE5IBoIgdEEzkgmsgB0by6CnQ5PHljrmP/RY77HmpnpeGTKuABbFeBaCIHRBt2u3qtewBgEVZyQDSRA6KJHBBN5IBoIgdEEzkgmsgB0UQOiCZyQDSRA6IN+7YuoN0in/orckCXRSJnuwpEs5LD+ZZEs5IDookcEE3kgGgiB0TzwsPQXqt6tXsGVuN69wCnI3JDe7Xq+e4ZWI1F/qrtbP24bFeBcFZyQzuoOjroHoJVmKqOu2fY1VSPl5UcEE7kgGgiB0QTOSCaFx6oqte+P8M3vTzD94SdiRxV9crRDN/U2SY8zOHJG3M9YGxXgWgiB0QTOSCayAHRRA6IJnJANJEDookcEE3kgGgiB0QTOSCayAHRRA6IJnJANJEDojlPDuiyyJmDIkdVXV3hxwbDdmxXgWgiB0QbcLv631VT9wx7xEaVba3092bAldzd7gGAhfy4hlzJffBm3eieYW9crr/5ZPcMrMLlWt/vzeV6dw0ZuanqUvcMe+SpS90TsBaXugfY3bkacrsKjETkgGgiB0Qb8Jocb3V01D1BtEXevMTbETmgy+HJG3M9F9iuAtFEDogmckA0kQOiiRwQTeSAaCIHRBM5IJrIAdFEDogmckA0kQOiiRwQTeSAaI5aArpcWuJORA7ocm2JOxE5quqqj5gmlmtyQDSRA6KJHBDNNbnRuRrHtqbuAXZ3q4ZcyZ3rHgBYyJ0aciV3p3uA/fLxi90TsAovVz3bPcOuXq4LNWTk2PClg+4JWIWp6nr3DLu6t78ecLsKjETkgGgiB0Qb9prcSzdn+KbeHQV7Z9jIAe0OT954YaY7ETmq6uioewK2dWmRkzuSuCYHRBM5IJrIAdFEDogmckA0kQOiiRwQTeSAaCIHRBM5IJrIAdFEDogmckA0p5AAXeY6XWmDlRwQTeSAaLarlHPbSWYlB0QTOSCayAHRhr0m59NAYAzDRo6f85ID25q6Bzgd21Ug1u0aMnLnugcAFnKrhtyu3ukeYL8cHXRPwCpMVcfdM+xqqsdqyJUcMBKRA6KJHBBN5IBoA77wwFsdHZ3991zkqDB4KJEDuhyevDHX06LtKhBN5IBoIgdEEzkgmsgB0UQOiCZyQDSRA6KJHBBN5IBoIgdEEzkgmsgB0UQOiOaoJaDLpSXuROSALteWuBORo6quXu2eAObimhwQTeSAaANuV/+n6qB7hj3ypxe7J2AtDroHOJ3peONW1fHbfWWMd/+0ewJgGb/+n7arQLgBt6sfvDHAcnVbUx0ddM/AKqxxmzfVY2UlB4QTOSCayAHRBrwmx1sdHc32rV+Y7TvDVkQO6LLx5DrXewtFDuiySORckwOiiRwQTeSAaCIHRBM55nWnewBGJ3LM6/vdAzA6kWNe7+segNGJHBBN5JjX6o7nIY3IMa9/7x6A0Ykc8/qv7gEYncgxr6e7B2B0Ise8Ptw9AKMTOeY1dQ/A6By1RFV94lL3BAxpkSNVRY6qunixewKYi+0qEE3kgGgiB0RzTW50cx2sT56VvlI+YOTu/civvLb1P5ABWKkf1pCRu11VVa/c3PofZEfuH3+7ewJW4YmqH3TPsKsn6vEaMnJseOy93ROwFit8qDxSXngAwokcEE3kgGiuyVFVR0ezfvur2a/dcGqHJ2/M9UZWKzkgmsgB0UQOiCZyQDSRe7g73QMApydyD+eTQ2HFRA6IJnJANJEDookcEE3kgGgiB0QTuYdziAGsmMgB0axSgC6LnMElckAXkZvT773RPcE++cSlWb/9k7N+d3hHw0buqe4B9srFi90TwFy88ABEEzkg2rDbVaqqXqt6tXsGVuN69wCnI3JDe7Xq+e4ZWI0VfujaT2vI7eqAPzIM6m4NuZK72z3AHjmoOjroHoJVmFZ4RvZUF8qyBggnckA0kQOiDXhNjvu98lr3BCet8CU89prIUa/c7J7gJJEbyOHJGy/MdCe2q0A0kQOiiRwQTeSAaCIHRBM5IJrIAdFEDogmckA0kQOiiRwQTeSAaCIHRBM5IJrIAdGcJwd0mesIuQ0iR13rHgBmZLsKRBM5INq08XmxU9cYADM4vn8lt7pPyAZ4Z/e98PDR7z3WM8eCfnj8RPcI++O1dz3ePQIr8fpPn+weYWc/uVD3b1fr5rd7ZgE4ex9+5i2RA8ji1VUgmsgB0UQOiCZyQDSRA6KJHBBN5IBoIgdEEzkgmsgB0UQOiCZyQDSRA6KJHBBN5IBoIgdE+z9F5qxVaZpiXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=1252x834 at 0x7F6DEDE235F8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_3class_img(img):\n",
    "    img = np.array(img)\n",
    "    img[img > 160] = 255\n",
    "    img[img <= 85] = 0\n",
    "\n",
    "    mask1 = img <=160 \n",
    "    mask2 = img > 85\n",
    "    img[mask1 & mask2] = 125\n",
    "    \n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((834, 1252), (834, 1252))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_label = get_3class_img(img_label)\n",
    "img_test = get_3class_img(img_test)\n",
    "np.array(img_label).shape, np.array(img_test).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tmp = np.ones(np.array(img_label).shape) * 255\n",
    "# for r_i in np.arange(img_label.shape[0]):\n",
    "#     for c_i in np.arange(img_label.shape[1]):\n",
    "#         true_val = img_label[r_i, c_i]\n",
    "#         test_val = img_test[r_i, c_i]\n",
    "# #         print(true_val,test_val)\n",
    "#         if true_val == test_val and test_val == 0:\n",
    "#             tmp[r_i, c_i] = 0\n",
    "\n",
    "# Image.fromarray(tmp).convert('L')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = pd.DataFrame(np.zeros((3, 3)), index=[0, 125, 255], columns=[0, 125, 255])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for r_i in np.arange(img_label.shape[0]):\n",
    "    for c_i in np.arange(img_label.shape[1]):\n",
    "        true_val = img_label[r_i, c_i]\n",
    "        test_val = img_test[r_i, c_i]\n",
    "        if test_val == 0:\n",
    "            cm.loc[0, 0] = cm.loc[0, 0] + 1\n",
    "        else:\n",
    "            cm.loc[true_val, test_val] = cm.loc[true_val, test_val] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>125</th>\n",
       "      <th>255</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48350.0</td>\n",
       "      <td>5494.0</td>\n",
       "      <td>51012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0.0</td>\n",
       "      <td>75454.0</td>\n",
       "      <td>25960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15037.0</td>\n",
       "      <td>822861.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0        125       255\n",
       "0    48350.0   5494.0   51012.0\n",
       "125      0.0  75454.0   25960.0\n",
       "255      0.0  15037.0  822861.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.to_csv('./paper_data/cm_0{0}.csv'.format(test_i))\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(255, 255)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_label[r_i, c_i], img_test[r_i, c_i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(255, 255)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_val, test_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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